Dev Tools
rocketride-server
A high-performance AI pipeline engine - build LLM workflows as portable .pipe files
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View guide →Turn domain documents into high-quality LLM fine-tuning, RAG and evaluation datasets through a visual UI.
easy-dataset is an open-source dev tools project by ConardLi (ConardLi/easy-dataset on GitHub). It has 14,981 GitHub stars and 1,533 forks, is released under the AGPL-3.0 license, and was last updated on GitHub on 1 May 2026.
Collect the PDFs, Word files, Markdown, EPUB or other documents you want to turn into training or evaluation data.
Start Easy Dataset with Docker Compose or npm, then open it in your browser at http://localhost:1717.
Connect an LLM API (or a local Ollama model), import your files, and let parsing, splitting and the domain label tree run.
Generate questions and answers, check quality scores, then export in Alpaca or ShareGPT format, generate a LLaMA Factory config, or upload to Hugging Face.
Parses PDF, Markdown, DOCX, TXT, EPUB and more, with multiple splitting algorithms (Markdown structure, recursive, fixed length, code-aware) and visual segmentation.
Auto-extracts questions from text segments and generates answers and chain-of-thought with an LLM, guided by a domain label tree built from your documents.
Builds single-turn QA, multi-turn dialogue and image QA datasets, plus data distillation straight from domain topics without uploading documents.
Generates evaluation sets (true/false, choice, short-answer, open-ended), runs judge-model scoring with custom rules, and offers a double-blind human arena.
Exports Alpaca, ShareGPT and other formats as JSON/JSONL, with balanced export per tag, one-click LLaMA Factory config generation and direct Hugging Face upload.
Background batch processing with a task center, token and API-call monitoring dashboards, custom project prompt templates and a 3-model testing playground.
Clone Easy Dataset and enter the project directory.
git clone https://github.com/ConardLi/easy-dataset.git
cd easy-datasetInstall dependencies and build the app (Docker users can skip this and use the official image instead).
npm install
npm run buildLaunch the server and open the UI in your browser.
npm run start
# then open http://localhost:1717It is developed by ConardLi and community contributors; an accompanying paper is on arXiv (2507.04009).
The software is open source under AGPL-3.0, so self-hosting is free; note AGPL conditions apply if you offer it as a network service.
It needs an LLM to generate questions and answers: you can use a paid provider's API key, or a local Ollama model at no API cost.
Easy Dataset replaces ad-hoc parsing/splitting/generation scripts with a visual pipeline, quality scoring and one-click exports to training formats and LLaMA Factory.
Clone the repo (or pull the Docker image), start it, open http://localhost:1717, connect a model, and import your first documents.
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